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Local LLM Guide

Model

Gemma 4 E2B Instruct

Start with a supported compressed build and short context. 16GB RAM estimate. Best use: Best for 8GB to 16GB users, edge devices, and beginner local installs.

2.3BMultimodal

Best use

Best for 8GB to 16GB users, edge devices, and beginner local installs.

Memory planning

16GB RAM estimate

Expected pace

Depends on your hardware, app, and conversation length

License

Apache 2.0; read the model’s terms before use

Who should use this model?

  • Best for 8GB to 16GB users, edge devices, and beginner local installs.

Choose something else when

  • Avoid for tiny 8GB machines needing full-quality long context, or projects requiring non-text outputs today.

What hardware makes sense?

The download is only part of the memory needed. Leave room for the app, the input it processes, and other open programs. For chat models, context includes the instructions, conversation, and answer. Longer context needs more memory.

CPU only
A possible starting point with a compressed model and short conversation; check the exact file size.
8GB system memory
Memory or response time may limit use. Try a smaller model first.
16GB system memory
A possible starting point with a compressed model and short conversation; check the exact file size.
32GB system memory
A possible starting point with a compressed model and short conversation; check the exact file size.
8GB GPU memory
Memory or response time may limit use. Try a smaller model first.
12GB GPU memory
Memory or response time may limit use. Try a smaller model first.
24GB GPU memory
A possible starting point with a compressed model and short conversation; check the exact file size.
Apple unified memory
A possible starting point with a compressed model and short conversation; check the exact file size.

Model facts that change the decision

Parameters
2.3B
Input
Text · Image · Audio
Published context limit
128,000 tokens
Model format
safetensors · GGUF · Ollama · MLX
Suggested compression
Q4_K_M for first local attempts unless a cited runtime recommends a different default.
Typical Q4 file
2.9GB before runtime and context overhead
Languages
Unknown

Using image input

This model needs an app or library that supports its image-processing components. Follow the exact model’s instructions for preparing image inputs. Some GGUF downloads also need a matching projector file; loading the text model alone does not establish image support.

First setup

Ollama

ollama run gemma4:e2b

LM Studio: Check that your installed app supports the exact model format and task before downloading it.

llama.cpp

Use a cited GGUF build with current llama.cpp-compatible tooling.

Conditions that change the answer

  • Official Q4_0 memory estimates cover model loading only and do not include full runtime overhead, supporting software, or conversation memory.
  • Long 128K and 256K contexts can require much more RAM or VRAM than the base model-size number suggests.
  • Audio input is available only on E2B, E4B, and 12B, not on 26B-A4B or 31B.
  • All variants generate text output; they do not generate image or audio output.
  • The 26B-A4B MoE activates only 3.8B parameters per token but still must load all parameters into memory.
  • Use the official Apache 2.0 license and Google policy links for legal review before treating commercial use as cleared.
  • Commercial use: permitted
  • Gemma 4 is Google DeepMind’s newest open Gemma family for local reasoning, coding, agents, and multimodal chat. It spans tiny edge models, a 12B audio-capable model, a fast MoE, and a stronger 31B dense model.

Read the controlling license text →